In the past decade, few computational approaches were available for incorporating pathway knowledge to interpret high-throughput datasets, and more recently, certain approaches have been proposed that incorporate pathway topology. For example, signaling pathway impact analysis (SPIA) uses a method analogous to Google's PageRank to determine the influence of a gene in a pathway. Consequently, more influence is placed on genes that link out to many other genes. SPIA was successfully applied to different cancer datasets (lung adenocarcinoma and breast cancer) and shown to outperform overrepresentation analysis and gene set enrichment analysis for identifying pathways known to be involved in these cancers. However, while SPIA provided significant advantages in interpreting cancer datasets using pathway topology, SPIA is generally limited to using only a single type of genome-wide data. Consequently, as information for gene copy number, DNA methylation, somatic mutations, mRNA expression, and microRNA expression are not integrated into SPIA, analytic and predictive value of SPIA remains highly restricted, particularly where a more global analysis is required.
Still further, all or almost all of the currently known pathway analyses fail to incorporate interdependencies among genes in a pathway that can increase the detection signal for pathway relevance. Additionally, most known models treat all gene alterations as equal, a premise that is likely no representative for most biological systems. Further complicating the issue is the fact that many functional nucleic acids (for example, microRNAs) are pleiotropic, acting in several pathways with different roles.
Therefore, even tough numerous systems and methods of pathway analysis known in the art, all or all of them suffer from one or more disadvantage. Consequently, there is still a need for improved systems and methods of pathway analysis.